Overview

Dataset statistics

Number of variables18
Number of observations218160
Missing cells2145379
Missing cells (%)54.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory30.0 MiB
Average record size in memory144.0 B

Variable types

Categorical3
Numeric15

Warnings

dep has a high cardinality: 101 distinct values High cardinality
date_de_passage has a high cardinality: 360 distinct values High cardinality
nbre_pass_corona is highly correlated with nbre_pass_corona_h and 1 other fieldsHigh correlation
nbre_pass_tot is highly correlated with nbre_pass_tot_h and 1 other fieldsHigh correlation
nbre_hospit_corona is highly correlated with nbre_hospit_corona_h and 1 other fieldsHigh correlation
nbre_pass_corona_h is highly correlated with nbre_pass_corona and 2 other fieldsHigh correlation
nbre_pass_corona_f is highly correlated with nbre_pass_corona and 1 other fieldsHigh correlation
nbre_pass_tot_h is highly correlated with nbre_pass_tot and 1 other fieldsHigh correlation
nbre_pass_tot_f is highly correlated with nbre_pass_tot and 1 other fieldsHigh correlation
nbre_hospit_corona_h is highly correlated with nbre_hospit_corona and 1 other fieldsHigh correlation
nbre_hospit_corona_f is highly correlated with nbre_hospit_coronaHigh correlation
nbre_acte_corona is highly correlated with nbre_acte_corona_h and 1 other fieldsHigh correlation
nbre_acte_tot is highly correlated with nbre_acte_tot_h and 1 other fieldsHigh correlation
nbre_acte_corona_h is highly correlated with nbre_acte_corona and 1 other fieldsHigh correlation
nbre_acte_corona_f is highly correlated with nbre_acte_corona and 1 other fieldsHigh correlation
nbre_acte_tot_h is highly correlated with nbre_acte_tot and 1 other fieldsHigh correlation
nbre_acte_tot_f is highly correlated with nbre_acte_tot and 1 other fieldsHigh correlation
nbre_pass_corona has 2487 (1.1%) missing values Missing
nbre_pass_tot has 2487 (1.1%) missing values Missing
nbre_hospit_corona has 2487 (1.1%) missing values Missing
nbre_pass_corona_h has 182165 (83.5%) missing values Missing
nbre_pass_corona_f has 182165 (83.5%) missing values Missing
nbre_pass_tot_h has 182165 (83.5%) missing values Missing
nbre_pass_tot_f has 182165 (83.5%) missing values Missing
nbre_hospit_corona_h has 182165 (83.5%) missing values Missing
nbre_hospit_corona_f has 182165 (83.5%) missing values Missing
nbre_acte_corona has 119258 (54.7%) missing values Missing
nbre_acte_tot has 119258 (54.7%) missing values Missing
nbre_acte_corona_h has 201603 (92.4%) missing values Missing
nbre_acte_corona_f has 201603 (92.4%) missing values Missing
nbre_acte_tot_h has 201603 (92.4%) missing values Missing
nbre_acte_tot_f has 201603 (92.4%) missing values Missing
dep is uniformly distributed Uniform
date_de_passage is uniformly distributed Uniform
sursaud_cl_age_corona is uniformly distributed Uniform
nbre_pass_corona has 102432 (47.0%) zeros Zeros
nbre_hospit_corona has 134855 (61.8%) zeros Zeros
nbre_pass_corona_h has 10619 (4.9%) zeros Zeros
nbre_pass_corona_f has 10785 (4.9%) zeros Zeros
nbre_hospit_corona_h has 15585 (7.1%) zeros Zeros
nbre_hospit_corona_f has 16943 (7.8%) zeros Zeros
nbre_acte_corona has 41397 (19.0%) zeros Zeros
nbre_acte_corona_h has 3358 (1.5%) zeros Zeros
nbre_acte_corona_f has 2825 (1.3%) zeros Zeros

Reproduction

Analysis started2021-02-18 21:46:48.429572
Analysis finished2021-02-18 21:47:20.497286
Duration32.07 seconds
Software versionpandas-profiling v2.10.0
Download configurationconfig.yaml

Variables

dep
Categorical

HIGH CARDINALITY
UNIFORM

Distinct101
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.7 MiB
26
 
2160
73
 
2160
974
 
2160
49
 
2160
89
 
2160
Other values (96)
207360 

Length

Max length3
Median length2
Mean length2.04950495
Min length2

Characters and Unicode

Total characters447120
Distinct characters12
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row01
2nd row01
3rd row01
4th row01
5th row01
ValueCountFrequency (%)
262160
 
1.0%
732160
 
1.0%
9742160
 
1.0%
492160
 
1.0%
892160
 
1.0%
762160
 
1.0%
902160
 
1.0%
792160
 
1.0%
942160
 
1.0%
132160
 
1.0%
Other values (91)196560
90.1%
2021-02-18T22:47:20.714001image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
262160
 
1.0%
732160
 
1.0%
492160
 
1.0%
892160
 
1.0%
762160
 
1.0%
902160
 
1.0%
792160
 
1.0%
942160
 
1.0%
132160
 
1.0%
122160
 
1.0%
Other values (91)196560
90.1%

Most occurring characters

ValueCountFrequency (%)
751840
11.6%
247520
10.6%
145360
10.1%
345360
10.1%
445360
10.1%
543200
9.7%
643200
9.7%
943200
9.7%
841040
9.2%
036720
8.2%
Other values (2)4320
 
1.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number442800
99.0%
Uppercase Letter4320
 
1.0%

Most frequent character per category

ValueCountFrequency (%)
751840
11.7%
247520
10.7%
145360
10.2%
345360
10.2%
445360
10.2%
543200
9.8%
643200
9.8%
943200
9.8%
841040
9.3%
036720
8.3%
ValueCountFrequency (%)
A2160
50.0%
B2160
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common442800
99.0%
Latin4320
 
1.0%

Most frequent character per script

ValueCountFrequency (%)
751840
11.7%
247520
10.7%
145360
10.2%
345360
10.2%
445360
10.2%
543200
9.8%
643200
9.8%
943200
9.8%
841040
9.3%
036720
8.3%
ValueCountFrequency (%)
A2160
50.0%
B2160
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII447120
100.0%

Most frequent character per block

ValueCountFrequency (%)
751840
11.6%
247520
10.6%
145360
10.1%
345360
10.1%
445360
10.1%
543200
9.7%
643200
9.7%
943200
9.7%
841040
9.2%
036720
8.2%
Other values (2)4320
 
1.0%

date_de_passage
Categorical

HIGH CARDINALITY
UNIFORM

Distinct360
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size1.7 MiB
2020-08-31
 
606
2020-09-28
 
606
2020-05-13
 
606
2020-05-15
 
606
2021-01-15
 
606
Other values (355)
215130 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters2181600
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2020-02-24
2nd row2020-02-24
3rd row2020-02-24
4th row2020-02-24
5th row2020-02-24
ValueCountFrequency (%)
2020-08-31606
 
0.3%
2020-09-28606
 
0.3%
2020-05-13606
 
0.3%
2020-05-15606
 
0.3%
2021-01-15606
 
0.3%
2020-11-17606
 
0.3%
2020-05-30606
 
0.3%
2020-08-24606
 
0.3%
2020-07-30606
 
0.3%
2020-04-17606
 
0.3%
Other values (350)212100
97.2%
2021-02-18T22:47:20.908329image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2020-08-31606
 
0.3%
2020-09-28606
 
0.3%
2020-05-13606
 
0.3%
2020-05-15606
 
0.3%
2021-01-15606
 
0.3%
2020-11-17606
 
0.3%
2020-05-30606
 
0.3%
2020-08-24606
 
0.3%
2020-07-30606
 
0.3%
2020-04-17606
 
0.3%
Other values (350)212100
97.2%

Most occurring characters

ValueCountFrequency (%)
0674478
30.9%
2560550
25.7%
-436320
20.0%
1218766
 
10.0%
350904
 
2.3%
540602
 
1.9%
740602
 
1.9%
439996
 
1.8%
639996
 
1.8%
839996
 
1.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1745280
80.0%
Dash Punctuation436320
 
20.0%

Most frequent character per category

ValueCountFrequency (%)
0674478
38.6%
2560550
32.1%
1218766
 
12.5%
350904
 
2.9%
540602
 
2.3%
740602
 
2.3%
439996
 
2.3%
639996
 
2.3%
839996
 
2.3%
939390
 
2.3%
ValueCountFrequency (%)
-436320
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common2181600
100.0%

Most frequent character per script

ValueCountFrequency (%)
0674478
30.9%
2560550
25.7%
-436320
20.0%
1218766
 
10.0%
350904
 
2.3%
540602
 
1.9%
740602
 
1.9%
439996
 
1.8%
639996
 
1.8%
839996
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII2181600
100.0%

Most frequent character per block

ValueCountFrequency (%)
0674478
30.9%
2560550
25.7%
-436320
20.0%
1218766
 
10.0%
350904
 
2.3%
540602
 
1.9%
740602
 
1.9%
439996
 
1.8%
639996
 
1.8%
839996
 
1.8%

sursaud_cl_age_corona
Categorical

UNIFORM

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.7 MiB
B
36360 
A
36360 
C
36360 
D
36360 
0
36360 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters218160
Distinct characters6
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd rowA
3rd rowB
4th rowC
5th rowD
ValueCountFrequency (%)
B36360
16.7%
A36360
16.7%
C36360
16.7%
D36360
16.7%
036360
16.7%
E36360
16.7%
2021-02-18T22:47:21.069784image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-02-18T22:47:21.121404image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
a36360
16.7%
b36360
16.7%
d36360
16.7%
e36360
16.7%
036360
16.7%
c36360
16.7%

Most occurring characters

ValueCountFrequency (%)
036360
16.7%
A36360
16.7%
B36360
16.7%
C36360
16.7%
D36360
16.7%
E36360
16.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter181800
83.3%
Decimal Number36360
 
16.7%

Most frequent character per category

ValueCountFrequency (%)
A36360
20.0%
B36360
20.0%
C36360
20.0%
D36360
20.0%
E36360
20.0%
ValueCountFrequency (%)
036360
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin181800
83.3%
Common36360
 
16.7%

Most frequent character per script

ValueCountFrequency (%)
A36360
20.0%
B36360
20.0%
C36360
20.0%
D36360
20.0%
E36360
20.0%
ValueCountFrequency (%)
036360
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII218160
100.0%

Most frequent character per block

ValueCountFrequency (%)
036360
16.7%
A36360
16.7%
B36360
16.7%
C36360
16.7%
D36360
16.7%
E36360
16.7%

nbre_pass_corona
Real number (ℝ≥0)

HIGH CORRELATION
MISSING
ZEROS

Distinct251
Distinct (%)0.1%
Missing2487
Missing (%)1.1%
Infinite0
Infinite (%)0.0%
Mean3.399493678
Minimum0
Maximum635
Zeros102432
Zeros (%)47.0%
Memory size1.7 MiB
2021-02-18T22:47:21.210733image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q33
95-th percentile15
Maximum635
Range635
Interquartile range (IQR)3

Descriptive statistics

Standard deviation10.55413546
Coefficient of variation (CV)3.104619823
Kurtosis438.964732
Mean3.399493678
Median Absolute Deviation (MAD)1
Skewness14.77292928
Sum733179
Variance111.3897753
MonotocityNot monotonic
2021-02-18T22:47:21.592210image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0102432
47.0%
134299
 
15.7%
219187
 
8.8%
312325
 
5.6%
48490
 
3.9%
56346
 
2.9%
64721
 
2.2%
73854
 
1.8%
83120
 
1.4%
92486
 
1.1%
Other values (241)18413
 
8.4%
(Missing)2487
 
1.1%
ValueCountFrequency (%)
0102432
47.0%
134299
 
15.7%
219187
 
8.8%
312325
 
5.6%
48490
 
3.9%
ValueCountFrequency (%)
6351
< 0.1%
5491
< 0.1%
5371
< 0.1%
5361
< 0.1%
5251
< 0.1%

nbre_pass_tot
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct1621
Distinct (%)0.8%
Missing2487
Missing (%)1.1%
Infinite0
Infinite (%)0.0%
Mean117.207082
Minimum0
Maximum2240
Zeros3
Zeros (%)< 0.1%
Memory size1.7 MiB
2021-02-18T22:47:21.725631image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile10
Q129
median59
Q3127
95-th percentile427
Maximum2240
Range2240
Interquartile range (IQR)98

Descriptive statistics

Standard deviation172.2271692
Coefficient of variation (CV)1.469426303
Kurtosis20.63477179
Mean117.207082
Median Absolute Deviation (MAD)37
Skewness3.859724206
Sum25278403
Variance29662.1978
MonotocityNot monotonic
2021-02-18T22:47:21.825471image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
172470
 
1.1%
202436
 
1.1%
192427
 
1.1%
182424
 
1.1%
242402
 
1.1%
232374
 
1.1%
222337
 
1.1%
282312
 
1.1%
262294
 
1.1%
162294
 
1.1%
Other values (1611)191903
88.0%
(Missing)2487
 
1.1%
ValueCountFrequency (%)
03
 
< 0.1%
1553
0.3%
2718
0.3%
3928
0.4%
4968
0.4%
ValueCountFrequency (%)
22401
< 0.1%
22271
< 0.1%
21881
< 0.1%
21021
< 0.1%
20901
< 0.1%

nbre_hospit_corona
Real number (ℝ≥0)

HIGH CORRELATION
MISSING
ZEROS

Distinct118
Distinct (%)0.1%
Missing2487
Missing (%)1.1%
Infinite0
Infinite (%)0.0%
Mean1.521252081
Minimum0
Maximum203
Zeros134855
Zeros (%)61.8%
Memory size1.7 MiB
2021-02-18T22:47:21.929837image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile7
Maximum203
Range203
Interquartile range (IQR)1

Descriptive statistics

Standard deviation4.447144177
Coefficient of variation (CV)2.92334468
Kurtosis179.0656161
Mean1.521252081
Median Absolute Deviation (MAD)0
Skewness9.607671325
Sum328093
Variance19.77709133
MonotocityNot monotonic
2021-02-18T22:47:22.049843image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0134855
61.8%
131870
 
14.6%
215090
 
6.9%
38800
 
4.0%
45889
 
2.7%
53888
 
1.8%
62838
 
1.3%
72136
 
1.0%
81631
 
0.7%
91311
 
0.6%
Other values (108)7365
 
3.4%
(Missing)2487
 
1.1%
ValueCountFrequency (%)
0134855
61.8%
131870
 
14.6%
215090
 
6.9%
38800
 
4.0%
45889
 
2.7%
ValueCountFrequency (%)
2031
< 0.1%
1931
< 0.1%
1681
< 0.1%
1571
< 0.1%
1501
< 0.1%

nbre_pass_corona_h
Real number (ℝ≥0)

HIGH CORRELATION
MISSING
ZEROS

Distinct144
Distinct (%)0.4%
Missing182165
Missing (%)83.5%
Infinite0
Infinite (%)0.0%
Mean4.947770524
Minimum0
Maximum320
Zeros10619
Zeros (%)4.9%
Memory size1.7 MiB
2021-02-18T22:47:22.165008image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q35
95-th percentile19
Maximum320
Range320
Interquartile range (IQR)5

Descriptive statistics

Standard deviation10.51199952
Coefficient of variation (CV)2.124593182
Kurtosis132.1073272
Mean4.947770524
Median Absolute Deviation (MAD)2
Skewness8.53524319
Sum178095
Variance110.5021339
MonotocityNot monotonic
2021-02-18T22:47:22.270362image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
010619
 
4.9%
15642
 
2.6%
23967
 
1.8%
32884
 
1.3%
42181
 
1.0%
51704
 
0.8%
61361
 
0.6%
71050
 
0.5%
8811
 
0.4%
9779
 
0.4%
Other values (134)4997
 
2.3%
(Missing)182165
83.5%
ValueCountFrequency (%)
010619
4.9%
15642
2.6%
23967
 
1.8%
32884
 
1.3%
42181
 
1.0%
ValueCountFrequency (%)
3201
< 0.1%
2801
< 0.1%
2541
< 0.1%
2441
< 0.1%
2402
< 0.1%

nbre_pass_corona_f
Real number (ℝ≥0)

HIGH CORRELATION
MISSING
ZEROS

Distinct152
Distinct (%)0.4%
Missing182165
Missing (%)83.5%
Infinite0
Infinite (%)0.0%
Mean5.235616058
Minimum0
Maximum315
Zeros10785
Zeros (%)4.9%
Memory size1.7 MiB
2021-02-18T22:47:22.391550image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q36
95-th percentile20
Maximum315
Range315
Interquartile range (IQR)6

Descriptive statistics

Standard deviation11.1820205
Coefficient of variation (CV)2.135760219
Kurtosis133.1954742
Mean5.235616058
Median Absolute Deviation (MAD)2
Skewness8.502372967
Sum188456
Variance125.0375824
MonotocityNot monotonic
2021-02-18T22:47:22.504496image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
010785
 
4.9%
15596
 
2.6%
23738
 
1.7%
32758
 
1.3%
42026
 
0.9%
51726
 
0.8%
61338
 
0.6%
71039
 
0.5%
8867
 
0.4%
9717
 
0.3%
Other values (142)5405
 
2.5%
(Missing)182165
83.5%
ValueCountFrequency (%)
010785
4.9%
15596
2.6%
23738
 
1.7%
32758
 
1.3%
42026
 
0.9%
ValueCountFrequency (%)
3151
< 0.1%
2921
< 0.1%
2851
< 0.1%
2831
< 0.1%
2691
< 0.1%

nbre_pass_tot_h
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct924
Distinct (%)2.6%
Missing182165
Missing (%)83.5%
Infinite0
Infinite (%)0.0%
Mean182.241728
Minimum0
Maximum1176
Zeros2
Zeros (%)< 0.1%
Memory size1.7 MiB
2021-02-18T22:47:22.620811image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile35
Q177
median134
Q3234
95-th percentile486
Maximum1176
Range1176
Interquartile range (IQR)157

Descriptive statistics

Standard deviation151.9718787
Coefficient of variation (CV)0.8339027527
Kurtosis4.17304466
Mean182.241728
Median Absolute Deviation (MAD)68
Skewness1.83307799
Sum6559791
Variance23095.4519
MonotocityNot monotonic
2021-02-18T22:47:22.737230image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
66219
 
0.1%
68211
 
0.1%
67203
 
0.1%
77201
 
0.1%
69200
 
0.1%
73199
 
0.1%
70198
 
0.1%
61198
 
0.1%
57198
 
0.1%
71197
 
0.1%
Other values (914)33971
 
15.6%
(Missing)182165
83.5%
ValueCountFrequency (%)
02
 
< 0.1%
114
< 0.1%
216
< 0.1%
312
< 0.1%
417
< 0.1%
ValueCountFrequency (%)
11761
< 0.1%
11561
< 0.1%
11391
< 0.1%
11191
< 0.1%
10781
< 0.1%

nbre_pass_tot_f
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct867
Distinct (%)2.4%
Missing182165
Missing (%)83.5%
Infinite0
Infinite (%)0.0%
Mean168.8579247
Minimum0
Maximum1084
Zeros9
Zeros (%)< 0.1%
Memory size1.7 MiB
2021-02-18T22:47:22.866519image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile32
Q173
median125
Q3217
95-th percentile450
Maximum1084
Range1084
Interquartile range (IQR)144

Descriptive statistics

Standard deviation139.4479773
Coefficient of variation (CV)0.8258302214
Kurtosis4.153146514
Mean168.8579247
Median Absolute Deviation (MAD)63
Skewness1.804841512
Sum6078041
Variance19445.73838
MonotocityNot monotonic
2021-02-18T22:47:22.982939image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
73250
 
0.1%
96218
 
0.1%
78217
 
0.1%
48214
 
0.1%
71211
 
0.1%
87211
 
0.1%
70211
 
0.1%
84210
 
0.1%
81210
 
0.1%
89208
 
0.1%
Other values (857)33835
 
15.5%
(Missing)182165
83.5%
ValueCountFrequency (%)
09
< 0.1%
118
< 0.1%
217
< 0.1%
313
< 0.1%
418
< 0.1%
ValueCountFrequency (%)
10841
< 0.1%
10511
< 0.1%
10491
< 0.1%
10461
< 0.1%
10451
< 0.1%

nbre_hospit_corona_h
Real number (ℝ≥0)

HIGH CORRELATION
MISSING
ZEROS

Distinct83
Distinct (%)0.2%
Missing182165
Missing (%)83.5%
Infinite0
Infinite (%)0.0%
Mean2.506431449
Minimum0
Maximum130
Zeros15585
Zeros (%)7.1%
Memory size1.7 MiB
2021-02-18T22:47:23.100112image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q33
95-th percentile10
Maximum130
Range130
Interquartile range (IQR)3

Descriptive statistics

Standard deviation5.159971503
Coefficient of variation (CV)2.058692451
Kurtosis73.25180808
Mean2.506431449
Median Absolute Deviation (MAD)1
Skewness6.288173487
Sum90219
Variance26.62530592
MonotocityNot monotonic
2021-02-18T22:47:23.218125image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
015585
 
7.1%
16660
 
3.1%
23849
 
1.8%
32479
 
1.1%
41596
 
0.7%
51208
 
0.6%
6887
 
0.4%
7661
 
0.3%
8537
 
0.2%
9421
 
0.2%
Other values (73)2112
 
1.0%
(Missing)182165
83.5%
ValueCountFrequency (%)
015585
7.1%
16660
3.1%
23849
 
1.8%
32479
 
1.1%
41596
 
0.7%
ValueCountFrequency (%)
1301
< 0.1%
1271
< 0.1%
1111
< 0.1%
1021
< 0.1%
961
< 0.1%

nbre_hospit_corona_f
Real number (ℝ≥0)

HIGH CORRELATION
MISSING
ZEROS

Distinct60
Distinct (%)0.2%
Missing182165
Missing (%)83.5%
Infinite0
Infinite (%)0.0%
Mean2.05075705
Minimum0
Maximum76
Zeros16943
Zeros (%)7.8%
Memory size1.7 MiB
2021-02-18T22:47:23.331926image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile9
Maximum76
Range76
Interquartile range (IQR)2

Descriptive statistics

Standard deviation3.999410507
Coefficient of variation (CV)1.950211756
Kurtosis37.78013143
Mean2.05075705
Median Absolute Deviation (MAD)1
Skewness4.801567812
Sum73817
Variance15.9952844
MonotocityNot monotonic
2021-02-18T22:47:23.438651image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
016943
 
7.8%
16574
 
3.0%
23699
 
1.7%
32431
 
1.1%
41561
 
0.7%
51128
 
0.5%
6785
 
0.4%
7551
 
0.3%
8424
 
0.2%
9336
 
0.2%
Other values (50)1563
 
0.7%
(Missing)182165
83.5%
ValueCountFrequency (%)
016943
7.8%
16574
 
3.0%
23699
 
1.7%
32431
 
1.1%
41561
 
0.7%
ValueCountFrequency (%)
761
< 0.1%
671
< 0.1%
651
< 0.1%
631
< 0.1%
591
< 0.1%

nbre_acte_corona
Real number (ℝ≥0)

HIGH CORRELATION
MISSING
ZEROS

Distinct156
Distinct (%)0.2%
Missing119258
Missing (%)54.7%
Infinite0
Infinite (%)0.0%
Mean3.657691452
Minimum0
Maximum259
Zeros41397
Zeros (%)19.0%
Memory size1.7 MiB
2021-02-18T22:47:23.544425image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q34
95-th percentile16
Maximum259
Range259
Interquartile range (IQR)4

Descriptive statistics

Standard deviation8.527124053
Coefficient of variation (CV)2.331285775
Kurtosis106.162822
Mean3.657691452
Median Absolute Deviation (MAD)1
Skewness7.519453693
Sum361753
Variance72.71184461
MonotocityNot monotonic
2021-02-18T22:47:23.654915image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
041397
 
19.0%
116800
 
7.7%
29333
 
4.3%
36009
 
2.8%
44340
 
2.0%
53257
 
1.5%
62570
 
1.2%
72038
 
0.9%
81718
 
0.8%
91361
 
0.6%
Other values (146)10079
 
4.6%
(Missing)119258
54.7%
ValueCountFrequency (%)
041397
19.0%
116800
7.7%
29333
 
4.3%
36009
 
2.8%
44340
 
2.0%
ValueCountFrequency (%)
2591
< 0.1%
2521
< 0.1%
2511
< 0.1%
2401
< 0.1%
2231
< 0.1%

nbre_acte_tot
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct907
Distinct (%)0.9%
Missing119258
Missing (%)54.7%
Infinite0
Infinite (%)0.0%
Mean61.49019231
Minimum1
Maximum1444
Zeros0
Zeros (%)0.0%
Memory size1.7 MiB
2021-02-18T22:47:23.764525image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q112
median28
Q372
95-th percentile224
Maximum1444
Range1443
Interquartile range (IQR)60

Descriptive statistics

Standard deviation95.96342943
Coefficient of variation (CV)1.560629847
Kurtosis25.42900746
Mean61.49019231
Median Absolute Deviation (MAD)20
Skewness4.214162516
Sum6081503
Variance9208.979788
MonotocityNot monotonic
2021-02-18T22:47:23.866848image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
92621
 
1.2%
72586
 
1.2%
82513
 
1.2%
62510
 
1.2%
102469
 
1.1%
52399
 
1.1%
112258
 
1.0%
122245
 
1.0%
132167
 
1.0%
42154
 
1.0%
Other values (897)74980
34.4%
(Missing)119258
54.7%
ValueCountFrequency (%)
1886
 
0.4%
21411
0.6%
31739
0.8%
42154
1.0%
52399
1.1%
ValueCountFrequency (%)
14441
< 0.1%
14141
< 0.1%
13791
< 0.1%
13091
< 0.1%
12621
< 0.1%

nbre_acte_corona_h
Real number (ℝ≥0)

HIGH CORRELATION
MISSING
ZEROS

Distinct79
Distinct (%)0.5%
Missing201603
Missing (%)92.4%
Infinite0
Infinite (%)0.0%
Mean4.771335387
Minimum0
Maximum121
Zeros3358
Zeros (%)1.5%
Memory size1.7 MiB
2021-02-18T22:47:23.989900image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q36
95-th percentile17
Maximum121
Range121
Interquartile range (IQR)5

Descriptive statistics

Standard deviation7.051266534
Coefficient of variation (CV)1.477839213
Kurtosis39.040743
Mean4.771335387
Median Absolute Deviation (MAD)2
Skewness4.663103465
Sum78999
Variance49.72035974
MonotocityNot monotonic
2021-02-18T22:47:24.104584image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
03358
 
1.5%
12519
 
1.2%
22082
 
1.0%
31699
 
0.8%
41355
 
0.6%
51041
 
0.5%
6728
 
0.3%
7665
 
0.3%
8484
 
0.2%
9385
 
0.2%
Other values (69)2241
 
1.0%
(Missing)201603
92.4%
ValueCountFrequency (%)
03358
1.5%
12519
1.2%
22082
1.0%
31699
0.8%
41355
0.6%
ValueCountFrequency (%)
1211
< 0.1%
1141
< 0.1%
1101
< 0.1%
1071
< 0.1%
1031
< 0.1%

nbre_acte_corona_f
Real number (ℝ≥0)

HIGH CORRELATION
MISSING
ZEROS

Distinct101
Distinct (%)0.6%
Missing201603
Missing (%)92.4%
Infinite0
Infinite (%)0.0%
Mean6.164643353
Minimum0
Maximum144
Zeros2825
Zeros (%)1.3%
Memory size1.7 MiB
2021-02-18T22:47:24.217386image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q38
95-th percentile22
Maximum144
Range144
Interquartile range (IQR)7

Descriptive statistics

Standard deviation9.075810152
Coefficient of variation (CV)1.472236046
Kurtosis37.312306
Mean6.164643353
Median Absolute Deviation (MAD)3
Skewness4.652615125
Sum102068
Variance82.37032992
MonotocityNot monotonic
2021-02-18T22:47:24.325859image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
02825
 
1.3%
12011
 
0.9%
21827
 
0.8%
31700
 
0.8%
41328
 
0.6%
51086
 
0.5%
6848
 
0.4%
7725
 
0.3%
8578
 
0.3%
9465
 
0.2%
Other values (91)3164
 
1.5%
(Missing)201603
92.4%
ValueCountFrequency (%)
02825
1.3%
12011
0.9%
21827
0.8%
31700
0.8%
41328
0.6%
ValueCountFrequency (%)
1441
 
< 0.1%
1383
< 0.1%
1241
 
< 0.1%
1221
 
< 0.1%
1201
 
< 0.1%

nbre_acte_tot_h
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct441
Distinct (%)2.7%
Missing201603
Missing (%)92.4%
Infinite0
Infinite (%)0.0%
Mean78.14392704
Minimum0
Maximum621
Zeros11
Zeros (%)< 0.1%
Memory size1.7 MiB
2021-02-18T22:47:24.436362image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile15
Q136
median55
Q392
95-th percentile232
Maximum621
Range621
Interquartile range (IQR)56

Descriptive statistics

Standard deviation70.51416285
Coefficient of variation (CV)0.9023626726
Kurtosis6.358756292
Mean78.14392704
Median Absolute Deviation (MAD)24
Skewness2.2748564
Sum1293829
Variance4972.247162
MonotocityNot monotonic
2021-02-18T22:47:24.543627image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
39270
 
0.1%
43264
 
0.1%
40260
 
0.1%
36246
 
0.1%
44241
 
0.1%
38232
 
0.1%
37230
 
0.1%
45229
 
0.1%
35229
 
0.1%
41228
 
0.1%
Other values (431)14128
 
6.5%
(Missing)201603
92.4%
ValueCountFrequency (%)
011
< 0.1%
17
 
< 0.1%
224
< 0.1%
323
< 0.1%
426
< 0.1%
ValueCountFrequency (%)
6211
< 0.1%
6201
< 0.1%
5651
< 0.1%
5551
< 0.1%
5522
< 0.1%

nbre_acte_tot_f
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct562
Distinct (%)3.4%
Missing201603
Missing (%)92.4%
Infinite0
Infinite (%)0.0%
Mean105.6936039
Minimum0
Maximum824
Zeros1
Zeros (%)< 0.1%
Memory size1.7 MiB
2021-02-18T22:47:24.660997image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile21
Q150
median75
Q3125
95-th percentile312
Maximum824
Range824
Interquartile range (IQR)75

Descriptive statistics

Standard deviation93.136463
Coefficient of variation (CV)0.8811929914
Kurtosis6.381608919
Mean105.6936039
Median Absolute Deviation (MAD)32
Skewness2.286216951
Sum1749969
Variance8674.40074
MonotocityNot monotonic
2021-02-18T22:47:24.763343image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
63198
 
0.1%
58195
 
0.1%
52193
 
0.1%
66191
 
0.1%
49191
 
0.1%
61191
 
0.1%
59189
 
0.1%
64189
 
0.1%
51189
 
0.1%
68186
 
0.1%
Other values (552)14645
 
6.7%
(Missing)201603
92.4%
ValueCountFrequency (%)
01
 
< 0.1%
15
< 0.1%
25
< 0.1%
310
< 0.1%
46
< 0.1%
ValueCountFrequency (%)
8241
< 0.1%
8231
< 0.1%
7931
< 0.1%
7421
< 0.1%
7071
< 0.1%

Interactions

2021-02-18T22:46:55.792105image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-18T22:46:55.909195image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-18T22:46:56.005931image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-18T22:46:56.098144image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-18T22:46:56.190310image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-18T22:46:56.281900image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-18T22:46:56.498514image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-18T22:46:56.635236image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-18T22:46:56.786550image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-18T22:46:56.923481image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-18T22:46:57.613609image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-18T22:46:58.592342image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-18T22:46:59.466124image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-18T22:46:59.592746image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-18T22:46:59.710959image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-18T22:46:59.812168image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-18T22:46:59.900941image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-18T22:46:59.992482image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-18T22:47:00.116965image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-18T22:47:00.243098image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-18T22:47:00.334501image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-18T22:47:00.419507image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-18T22:47:00.511853image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-18T22:47:00.600085image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-18T22:47:00.685518image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-18T22:47:00.768088image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-18T22:47:00.852932image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-18T22:47:00.935629image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-18T22:47:01.036841image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-18T22:47:01.156991image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-18T22:47:01.249212image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-18T22:47:01.345366image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-18T22:47:01.442693image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-18T22:47:01.539588image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-18T22:47:01.634265image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-18T22:47:01.828455image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-18T22:47:01.949109image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-18T22:47:02.049770image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-18T22:47:02.146819image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-18T22:47:02.240958image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-18T22:47:02.337291image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-18T22:47:02.431825image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-18T22:47:02.525404image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-18T22:47:02.616998image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-18T22:47:02.704410image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-18T22:47:02.800349image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-18T22:47:02.892511image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-18T22:47:02.983460image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-18T22:47:03.072851image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-18T22:47:03.163733image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-18T22:47:03.255705image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-18T22:47:03.342723image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-18T22:47:03.434344image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-18T22:47:03.524146image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-18T22:47:03.616441image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-18T22:47:03.706607image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-18T22:47:03.794340image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-18T22:47:03.886487image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-18T22:47:03.973868image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
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2021-02-18T22:47:04.164518image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-18T22:47:04.255594image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
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2021-02-18T22:47:04.436692image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-18T22:47:04.528715image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-18T22:47:04.615821image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
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2021-02-18T22:47:04.978699image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-18T22:47:05.189339image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
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2021-02-18T22:47:08.069599image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-18T22:47:08.161034image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-18T22:47:08.254071image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
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2021-02-18T22:47:11.092957image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
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2021-02-18T22:47:11.450200image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
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2021-02-18T22:47:11.624326image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
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2021-02-18T22:47:18.489068image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Correlations

2021-02-18T22:47:24.862887image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-02-18T22:47:25.033666image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-02-18T22:47:25.218317image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-02-18T22:47:25.390442image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2021-02-18T22:47:18.817646image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
A simple visualization of nullity by column.
2021-02-18T22:47:19.259812image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2021-02-18T22:47:19.978137image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2021-02-18T22:47:20.277877image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

depdate_de_passagesursaud_cl_age_coronanbre_pass_coronanbre_pass_totnbre_hospit_coronanbre_pass_corona_hnbre_pass_corona_fnbre_pass_tot_hnbre_pass_tot_fnbre_hospit_corona_hnbre_hospit_corona_fnbre_acte_coronanbre_acte_totnbre_acte_corona_hnbre_acte_corona_fnbre_acte_tot_hnbre_acte_tot_f
0012020-02-2400.0357.00.00.00.0202.0155.00.00.0NaNNaNNaNNaNNaNNaN
1012020-02-24A0.073.00.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
2012020-02-24B0.0155.00.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
3012020-02-24C0.061.00.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
4012020-02-24D0.028.00.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
5012020-02-24E0.040.00.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
6012020-02-2500.0310.00.00.00.0177.0133.00.00.0NaNNaNNaNNaNNaNNaN
7012020-02-25A0.070.00.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
8012020-02-25B0.0117.00.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
9012020-02-25C0.053.00.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN

Last rows

depdate_de_passagesursaud_cl_age_coronanbre_pass_coronanbre_pass_totnbre_hospit_coronanbre_pass_corona_hnbre_pass_corona_fnbre_pass_tot_hnbre_pass_tot_fnbre_hospit_corona_hnbre_hospit_corona_fnbre_acte_coronanbre_acte_totnbre_acte_corona_hnbre_acte_corona_fnbre_acte_tot_hnbre_acte_tot_f
2181509762021-02-16B10.044.02.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
2181519762021-02-16C6.015.02.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
2181529762021-02-16D3.04.03.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
2181539762021-02-16E4.04.02.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
2181549762021-02-1708.064.01.04.04.036.028.00.01.0NaNNaNNaNNaNNaNNaN
2181559762021-02-17A0.021.00.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
2181569762021-02-17B3.028.01.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
2181579762021-02-17C3.011.00.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
2181589762021-02-17D2.03.00.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
2181599762021-02-17E0.01.00.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN